Mask detection method and device, computer readable storage medium, computer program product and terminal

Abstract

The invention discloses a mask detection method and device, a computer readable storage medium, a computer program product and a terminal, and the method comprises the steps: obtaining a target mask which is a mask after optical proximity correction; obtaining a photomask image, wherein the photomask image is an image of a mask plate manufactured based on the target mask; and detecting the consistency of the photomask image and the target mask according to the image difference between the photomask image and the target mask. According to the scheme of the invention, the quality control of the mask can be realized in two dimensions, so that the manufacturing quality of the mask can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of integrated circuits, and in particular to a mask detection method and device, a computer-readable storage medium, a computer program product, and a terminal. Background Art

[0002] The integrated circuit manufacturing process mainly includes three stages: chip design, chip manufacturing and packaging testing. In the chip design stage, professional software is used to design the circuit diagram, and the physical design of the integrated circuit, such as multiple components and the interconnection lines between the components, is described through the integrated circuit layout design. In the chip manufacturing stage, layers of different materials are deposited on the surface of the wafer. The manufacturing process of each layer includes creating a corresponding mask (also called a photomask) based on the integrated circuit layout design and making a mask plate (also called a photomask, mask, photomask), transferring the pattern of the mask plate to the photoresist pre-coated on the surface of the layer through the photolithography process, and then etching, doping and other processes are performed to obtain the physical structure of the integrated circuit in this layer.

[0003] The traditional mask production process generally includes the steps of exposure, development, photoresist removal and photoetching. In this process, the designed circuit pattern (i.e., mask) is projected onto the photoresist through the exposure system. Due to the imperfection of the optical system and the diffraction effect, the pattern actually projected onto the photoresist is not completely consistent with the pattern on the mask. If these distortions are not corrected, they may greatly change the electrical performance of the produced circuit, because the circuit is made based on the distorted mask. To this end, on the one hand, the optical proximity correction (OPC) technology is used to correct the pattern on the mask so that the pattern projected onto the photoresist meets the design requirements as much as possible; on the other hand, the mask manufacturer will also measure the critical dimensions (also known as the wire diameter dimensions) of the pattern on the produced mask to control the quality of the mask. Specifically, when creating the mask, the spatial tolerance (i.e., critical dimensions) between components or interconnects is defined to ensure that the components or wires do not interact with each other in an undesirable manner. The mask manufacturer measures whether the wire diameter dimensions at the corresponding positions in the pattern on the mask meet expectations.

[0004] At present, measurement technologies such as critical dimension scanning electron microscope (CD-SEM) are commonly used to measure line diameter. Due to the characteristics of measurement technology, only one-dimensional measurement can be performed on the pattern on the mask. In fact, the pattern on the mask (especially the pattern after OPC processing) has a complex two-dimensional geometric shape and irregular distribution. A simple one-dimensional measurement cannot accurately characterize the quality of the mask, which is not conducive to the quality control of the mask. Summary of the invention

[0005] The technical problem solved by the present invention is how to improve the quality control of the mask.

[0006] To solve the above technical problems, an embodiment of the present invention provides a mask detection method, comprising: obtaining a target mask, wherein the target mask is a mask that has undergone optical proximity correction; obtaining a mask image, wherein the mask image is an image of a mask plate obtained based on the target mask; and detecting the consistency of the mask image and the target mask based on the image difference between the mask image and the target mask.

[0007] Optionally, the detection method further comprises: adjusting a value of at least one parameter used in optical proximity correction according to the image difference to obtain an updated target mask.

[0008] Optionally, detecting the consistency of the mask image and the target mask based on the image difference between the mask image and the target mask includes: overlapping the mask image and the target mask to obtain an overlapping image, wherein the overlapping image is divided into multiple segments according to the pattern contour of the target mask; for each segment, calculating the image difference between the pattern of the mask image and the pattern of the target mask in the segment; and detecting the consistency of the mask image and the target mask based on the image difference of the multiple segments.

[0009] Optionally, acquiring the target mask includes: acquiring a regional image of at least one first preset area of ​​the target mask; acquiring the mask image includes: acquiring a regional image of at least one second preset area of ​​the mask image, the at least one first preset area and the at least one second preset area corresponding one to one; overlapping the mask image and the target mask to obtain an overlapping image includes: for each of the first preset areas, overlapping the regional image of the first preset area and the corresponding regional image of the second preset area to obtain a corresponding overlapping regional image; and summarizing at least one of the overlapping regional images to obtain the overlapping image.

[0010] Optionally, detecting the consistency between the reticle image and the target mask according to the image differences of the multiple segments includes: statistically analyzing the numerical distribution of the image differences of the multiple segments; and detecting the consistency between the reticle image and the target mask according to the statistical results.

[0011] Optionally, the more dispersed the numerical distribution of the image differences of the multiple segments is and the farther the peak is from a baseline, the worse the consistency between the mask image and the target mask is, wherein the baseline is used to characterize that the image difference is zero.

[0012] In order to solve the above technical problems, an embodiment of the present invention also provides a mask detection device, including: a first acquisition module, used to acquire a target mask, wherein the target mask is a mask that has undergone optical proximity correction; a second acquisition module, used to acquire a mask image, wherein the mask image is an image of a mask plate obtained based on the target mask; and a detection module, used to detect the consistency between the mask image and the target mask based on the image difference between the mask image and the target mask.

[0013] To solve the above technical problems, an embodiment of the present invention further provides a computer-readable storage medium, which is a non-volatile storage medium or a non-transient storage medium, on which a computer program is stored, and when the computer program is run by a processor, the steps of the above method are executed.

[0014] To solve the above technical problem, an embodiment of the present invention further provides a computer program product, including a computer program / instruction, which implements the steps of the above method when executed by a processor.

[0015] To solve the above technical problem, an embodiment of the present invention further provides a terminal, including a memory and a processor, wherein the memory stores a computer program that can be executed on the processor, and the processor executes the steps of the above method when running the computer program.

[0016] Compared with the prior art, the technical solution of the embodiment of the present invention has the following beneficial effects:

[0017] An embodiment of the present invention provides a mask detection method, comprising: acquiring a target mask, wherein the target mask is a mask that has undergone optical proximity correction; acquiring a mask image, wherein the mask image is an image of a mask plate produced based on the target mask; and detecting the consistency of the mask image and the target mask based on an image difference between the mask image and the target mask.

[0018] Compared with the prior art which only measures one-dimensional wire diameter size data as the basis for mask quality control and cannot fully reflect the mask quality, this embodiment realizes two-dimensional mask quality control by detecting the image difference between the pattern on the target mask and the pattern actually produced on the mask plate, so as to help improve the production quality of the mask.

[0019] Furthermore, the photomask image and the target mask are overlapped to obtain an overlapped image, and the overlapped image is divided into multiple segments according to the pattern contour of the target mask; for each segment, the image difference between the pattern of the photomask image and the pattern of the target mask in the segment is calculated; the numerical distribution of the image difference of the multiple segments is statistically analyzed; and the consistency of the photomask image and the target mask is detected according to the statistical results. Thus, by combining image capture with statistical mathematics, the specific degree of difference between the graphics of the photomask image and the target mask in each segment is intuitively and comprehensively characterized based on the statistical results. The statistical results can be used as one of the bases for mask quality control to help optimize mask production. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 is a flow chart of a mask detection method according to an embodiment of the present invention;

[0021] Figure 2 is a schematic diagram of a typical application scenario of an embodiment of the present invention;

[0022] Figure 3 yes Figure 1 A flowchart of a specific implementation of step S103;

[0023] Figure 4 yes Figure 2 A distribution diagram of image difference values ​​of the overlapping area images shown;

[0024] Figure 5 It is a structural schematic diagram of a mask detection device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0025] As mentioned in the background, OPC is a photolithography resolution enhancement technology that has been widely used in the integrated circuit production process. Specifically, OPC adjusts the amplitude of light transmitted through the photolithography mask by modifying the mask layout design data used to create the mask. For example, the edges in the mask layout design can be adjusted according to how much extra exposure (or lack of exposure) is expected at certain points on the substrate to make certain parts of the geometric elements larger or smaller.

[0026] By checking whether the line diameter of the pattern on the mask is as expected, the quality of the mask can be controlled. For example, according to the measurement results of the line diameter, the various parameters of the OPC processing of the mask are adjusted to optimize the pattern on the mask, so that the pattern projected onto the photosensitive resin using the mask is as close as possible to the pattern on the mask, so as to avoid distortion of the pattern on the final mask.

[0027] Existing measurement technology has limitations and can only measure the wire diameter size of the pattern on the mask in one dimension. The inventor of this application has found through research that the defect points found in the test on the final integrated circuit are two-dimensional patterns, and all the wire diameter size measurement results of the area where the pattern is located on the mask in one dimension meet the requirements. It can be seen that the existing method of measuring the quality of the mask only by one-dimensional wire diameter size cannot meet the quality control requirements of the mask.

[0028] To solve the above technical problems, an embodiment of the present invention provides a mask detection method, comprising: obtaining a target mask, wherein the target mask is a mask that has undergone optical proximity correction; obtaining a mask image, wherein the mask image is an image of a mask plate obtained based on the target mask; and detecting the consistency of the mask image and the target mask based on the image difference between the mask image and the target mask.

[0029] This embodiment realizes two-dimensional quality control of the mask by detecting the image difference between the pattern on the target mask and the pattern obtained by projecting the target mask onto the wafer surface, so as to help improve the manufacturing quality of the mask.

[0030] In order to make the above-mentioned objects, features and beneficial effects of the present invention more obvious and easy to understand, specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0031] Figure 1 The present invention is a flowchart of a method for detecting a mask according to an embodiment of the present invention.

[0032] This embodiment can be applied to chip design and manufacturing scenarios, and can be specifically applied to mask production scenarios. By executing this embodiment, the quality of the mask can be controlled.

[0033] In a specific implementation, the detection method provided in the following steps S101 to S103 may be executed by a terminal, which may be, for example, a computer, a server, or other equipment.

[0034] Specifically, refer to Figure 1 The mask detection method described in this embodiment may include the following steps:

[0035] Step S101, obtaining a target mask, wherein the target mask is a mask after optical proximity correction;

[0036] Step S102, obtaining a mask image, wherein the mask image is an image of a mask plate produced based on the target mask;

[0037] Step S103 , detecting the consistency of the reticle image and the target mask according to the image difference between the reticle image and the target mask.

[0038] In practical applications, step S101 and step S102 may be executed synchronously or asynchronously. When executed asynchronously, the execution order of the two steps may be interchangeable.

[0039] More specifically, combined with Figure 2 The black wireframe exemplarily shows the target pattern of the circuit in the integrated circuit layout design plan, and the mask is created based on this using tools such as electronic design automation (EDA). Furthermore, the OPC model is integrated into EDA. Figure 2 The middle blue-gray filled area exemplarily shows a partial graphic of the target mask output after OPC model processing, where the red dots represent convex corner fragments, the blue dots represent concave corner fragments, and the green dots represent ripple fragments.

[0040] The target mask may be pre-created and stored in a database. When executing step S101, the terminal may access the database to retrieve the target mask. In some embodiments, the database may be stored locally in the terminal, or the database may be set independently of the terminal, such as stored in the cloud.

[0041] Furthermore, a mask plate can be manufactured based on the target mask. For example, quartz glass can be used as a substrate, on which a layer of metal chromium and photosensitive resin are plated to form a photosensitive material, and the target mask is exposed on the photosensitive resin through an electronic laser device, and the exposed area is developed to form a circuit pattern on the metal chromium. The projected circuit is photoetched by an integrated circuit lithography machine to obtain a mask plate. After exposure, or after photoetching, an image of the mask plate surface can be collected by, for example, a SEM to obtain a mask image.

[0042] Ideally, the pattern on the mask plate presented by the mask image should be exactly the same as the pattern on the target mask. However, in actual applications, due to the influence of many factors such as the manufacturing process and materials, there are more or less differences in the pattern contours of the mask image and the target mask. Figure 2 The red dotted line in the middle exemplarily shows a partial pattern outline of the mask image.

[0043] After the mask image is collected, it can be optimized by energy, focal length, distortion correction, etc. to improve the clarity and image accuracy of the mask image. In some embodiments, multiple mask images can be collected and averaged as the final mask image, wherein the averaging operation can be, for example, traversing all pixels in the image, and for each pixel, averaging the pixel values ​​of multiple mask images at the pixel point as the pixel value of the pixel point in the final mask image.

[0044] Furthermore, after the mask is projected onto the wafer surface through an exposure system, for example, onto the photoresist layer coated on the wafer surface, an image of the wafer surface can be acquired using, for example, a SEM to obtain a wafer image. Ideally, the circuit pattern on the wafer (i.e., the wafer image) obtained based on the mask should fit the target pattern as closely as possible, i.e. Figure 2 The pattern shown in the black frame in the middle. The greater the deviation between the aforementioned mask image and the pattern outline of the target mask, the greater the deviation between the wafer image and the target pattern.

[0045] refer to Figure 2 , the pattern outline of the mask image and the pattern outline of the target mask do not completely overlap, which will affect the electrical performance of the final chip. Figure 2 Taking the middle area W as an example, the minimum distance D between adjacent pattern contours in this area is a very important monitoring indicator. If the value of the minimum distance D is too small, it means that the spacing between the patterns is too close, which may cause components or interconnects to be unexpectedly connected and cause circuit failure. However, the existing one-dimensional wire diameter dimension measurement can only measure the dimensions in the x direction or y direction (such as the wire diameter dimension C1 and the wire diameter dimension C2 in the figure), and cannot directly measure the minimum distance D. Even if the values ​​of the wire diameter dimension C1 and the wire diameter dimension C2 meet the requirements, if the minimum distance D is too small, the mask image obtained based on the target mask projection is still unqualified.

[0046] The disclosed solution detects by executing step S103 Figure 2 The difference between the pattern outline of the mask image in area W and the pattern outline of the target mask, if the pattern outline of the mask image at the two corners in area W protrudes outward from the pattern outline of the target mask, it means that the minimum distance D is obviously smaller than the expected requirement. Therefore, based on the difference detection results of the mask image and the target mask, the quality of the mask pattern can be intuitively analyzed in two dimensions, making the minimum distance D that was originally unmeasurable in one dimension measurable.

[0047] Next, the specific implementation of step S103 is described in detail.

[0048] In one specific implementation, reference Figure 3 , step S103 may include the following steps:

[0049] Step S1031, overlapping the mask image and the target mask to obtain an overlapping image, wherein the overlapping image is divided into a plurality of segments according to a pattern outline of the target mask;

[0050] Step S1032, for each segment, calculating the image difference between the pattern of the mask image and the pattern of the target mask in the segment;

[0051] Step S1033 , detecting the consistency between the mask image and the target mask according to the image differences of the multiple segments.

[0052] Specifically, continue to refer to Figure 2 , for any color point among green, blue and red points, the pattern outline between two adjacent points can be regarded as a segment. For each segment, the pattern outline of the wafer pattern at the segment and the pattern outline of the target mask at the segment are overlapped to obtain the overlapping image at the segment, such as Figure 2 As shown in segments s1 and s2.

[0053] Furthermore, the image difference can be used to characterize the degree of pattern misalignment, that is, the deviation of the pattern profile of the mask image compared to the pattern profile of the target mask.

[0054] In some embodiments, the mask image and the target mask are presented with the same image magnification, and in step S1031 , the mask image and the target mask are overlapped based on preset coordinate points to obtain an overlapped image.

[0055] Specifically, the preset coordinate point, image magnification, and OPC Monitor Pattern (OMP) can be defined together in the CD on Mask table (CDM table). The mask manufacturer collects the mask image according to the preset coordinate point and image magnification marked in the CDM table. For example, the preset coordinate point can be the center point of the image. When collecting the mask image, the preset coordinate point is used as the center point, and the image is collected according to the image magnification marked in the CDM table.

[0056] In response to obtaining a reticle image and a target mask collected with the same image magnification, the two images are overlapped with each other based on a center point to obtain an overlapped image.

[0057] Furthermore, the CDM table may also include a target mask so that the collector of the mask image (eg, the mask manufacturer) can check whether the collected mask image meets the requirements.

[0058] In a specific implementation, step S101 may include: acquiring a regional image of at least one first preset area of ​​the target mask; step S102 may include: acquiring a regional image of at least one second preset area of ​​the mask image, the at least one first preset area and the at least one second preset area corresponding one to one.

[0059] Specifically, the first preset area may be a risk point on the target mask, that is, an area that needs to be focused on. In some embodiments, the risk point on the target mask may be determined based on the defect locations detected on the integrated circuit manufactured based on the target mask in the past, for example Figure 2 The middle area W may be determined as the first preset area. Further, the first preset area may be determined in combination with the Mask Rule Check (MRC) used when making the mask.

[0060] Further, after determining the first preset area, the preset coordinate points and image magnification of the first preset area can be recorded in the CDM table, and the image acquired according to the preset coordinate points and image magnification when acquiring the mask image is the regional image of the second preset area. In other words, the preset coordinate points and image magnification of at least one first preset area can be recorded in the CDM table, and accordingly, when acquiring the mask image, the image acquisition operation can be performed separately by traversing at least one preset coordinate point to obtain the regional image of at least one second preset area.

[0061] Further, step S1031 may include: for each of the first preset areas, overlapping the area image of the first preset area and the corresponding area image of the second preset area to obtain a corresponding overlapping area image; and aggregating at least one of the overlapping area images to obtain the overlapping image.

[0062] For example, Figure 2 An overlapping area image obtained by overlapping the area images of the first preset area and the corresponding second preset area is exemplarily displayed.

[0063] In some embodiments, after obtaining each overlapping region image, the image difference between the pattern outline of the target mask and the pattern outline of the reticle image in each overlapping region image may be detected respectively, and then the detection results may be summarized.

[0064] Alternatively, at least one overlapping region image may be spliced ​​to obtain a complete overlapping image, and then the image difference between the pattern outline of the target mask and the pattern outline of the mask image in the overlapping image may be detected to obtain a detection result.

[0065] In a specific implementation, in step S1032, the segmentation point center of the segment (eg, the midpoint of the segment) may be determined, and the deviation distance between the pattern outline of the target mask and the pattern outline of the mask image at the segmentation point center may be calculated.

[0066] For example, continue to refer to Figure 2, the image difference between the target mask and the mask image at the segmentation point center a1 of segment s1 is recorded as A1, and a positive triangle indicates that the image difference A1 is a negative value, that is, the pattern outline of the mask image at a1 deviates from the pattern outline of the target mask at a1 by a distance |A1|, where || is the absolute value symbol, and the segmentation point center a1 can be the midpoint of the line connecting the two vertices (red and green dots) of segment s1. Similarly, the image difference between the target mask and the mask image at the segmentation point center a2 of segment s2 is recorded as A2, and an inverted triangle indicates that the image difference A2 is a positive value, that is, the pattern outline of the mask image at a2 deviates from the pattern outline of the target mask at a2 by a distance |A2|, and the segmentation point center a2 can be the midpoint of the line connecting the two vertices (green dots and green dots) of segment s2.

[0067] In a specific implementation, step S1033 may include: calculating the numerical distribution of the image differences of the plurality of segments; and detecting the consistency between the mask image and the target mask according to the statistical result.

[0068] Still Figure 2 Taking the overlapping area image shown in the figure as an example, the image difference detection data of each segment in the figure are summarized, and the following can be obtained: Figure 4 The numerical distribution diagram is shown. Figure 4 In the figure, the horizontal axis is the image difference, and the vertical axis is the counting result. According to the value range of [-2.5, -2.0), [-2.0, -1.5), [-1.5, -1.0), [-1.0, -0.5), [-0.5, 0.0), [0.0, 0.5), [0.5, 1.0), [1.0, 1.5), [1.5, 2.0), [2.0, 2.5), [2.5, 3.0], the number of segments whose image difference falls into each value range can be counted. In this embodiment, the unit of distance is nanometer (nm).

[0069] Furthermore, the more dispersed the numerical distribution of the image differences of the multiple segments is and the farther the peak is from the baseline, the worse the consistency between the mask image and the target mask is, wherein the baseline is used to indicate that the image difference is zero. Figure 4 , the position where the image difference = 0.0 can be determined as the baseline, and it can be seen Figure 4 The numerical distribution shown is close to a normal distribution, and the peak value in the value range [-0.5, 0.0) is close to the baseline, so it can be determined that the consistency between the reticle image and the target mask is generally good.

[0070] Further reference Figure 4 , we can see that a certain number of image differences fall into the range [1.0,1.5) and [1.5,2.0) which are far from the baseline. Figure 2It can be seen that the difference between the images falling into these two value ranges is Figure 2 The image difference at the two corners of the middle area W.

[0071] In a specific implementation, the detection result of step S103 can be used as one of the mask quality control specifications to help improve the quality of mask production. Specifically, after step S103, this embodiment can also include the step of: adjusting the value of at least one parameter used in optical proximity correction according to the image difference to obtain an updated target mask.

[0072] Specifically, the at least one parameter may be a parameter used in the OPC model. By adjusting the value of the at least one parameter, the correction amount of the mask layout design data may be adjusted, thereby changing the pattern profile of the updated target mask.

[0073] For example, by Figure 4 The statistical results shown can intuitively and accurately determine Figure 2 The mask at the middle area W needs to be optimized. Accordingly, the value of at least one parameter used by the OPC model when performing OPC processing on the mask at the area W can be adjusted to adjust the pattern profile of the updated target mask at the area W.

[0074] Furthermore, the detection process and the process of updating the target mask according to the detection result described in this embodiment can be performed in a cyclic and iterative manner. For example, after one round of operation, the updated target mask and the mask image of the mask plate made based on the updated target mask are Figure 2 If the image differences at the two corners of the middle area W are reduced to 1.2 and 0.8 respectively, another round of operation can be performed to further reduce the target mask and the reticle image by continuing to adjust the parameters of the OPC model. Figure 2 Image difference in area W.

[0075] As described above, by adopting this embodiment, by detecting the image difference between the pattern on the target mask and the pattern actually produced on the mask plate, the quality control of the mask is achieved in two dimensions, so as to help improve the production quality of the mask.

[0076] In a variation, the image difference between the target pattern and the wafer image may be compared, and the degree of distortion of the pattern on the mask compared to the target mask may be determined based on the image difference between the two, which may also achieve quality control of the mask.

[0077] Figure 5 FIG. 5 is a schematic diagram of the structure of a mask detection device 5 according to an embodiment of the present invention. A person skilled in the art will appreciate that the mask detection device 5 according to this embodiment can be used to implement the above Figures 1 to 4 The method and technical solution described in the embodiments.

[0078] Specifically, refer to Figure 5 The mask detection device 5 described in this embodiment may include: a first acquisition module 51, used to acquire a target mask, wherein the target mask is a mask that has undergone optical proximity correction; a second acquisition module 52, used to acquire a mask image, wherein the mask image is an image of a mask plate obtained based on the target mask; and a detection module 53, used to detect the consistency between the mask image and the target mask according to the image difference between the mask image and the target mask.

[0079] For more information about the working principle and working method of the mask detection device 5, please refer to the above Figures 1 to 4 The relevant description in will not be repeated here.

[0080] Furthermore, an embodiment of the present invention also discloses a computer-readable storage medium having a computer program stored thereon, wherein the computer program is executed by a processor to perform the above Figures 1 to 4 Any method or technical solution described in the illustrated embodiments. Preferably, the computer-readable storage medium may include a computer-readable storage medium such as a non-volatile memory or a non-transitory memory. The computer-readable storage medium may include a ROM, a RAM, a magnetic disk or an optical disk, etc.

[0081] Furthermore, an embodiment of the present invention also discloses a terminal, including a memory and a processor, wherein the memory stores a computer program that can be run on the processor, and the processor executes the above-mentioned Figures 1 to 4 The method and technical solution described in any one of the embodiments shown in the figure. Specifically, the terminal can be a computer, a server or other equipment.

[0082] Furthermore, the embodiment of the present invention also discloses a computer program product, including a computer program / instruction, which implements the above-mentioned Figures 1 to 4 Any method or technical solution described in the embodiments shown.

[0083] Although the present invention is disclosed as above, the present invention is not limited thereto. Any person skilled in the art can make various changes and modifications without departing from the spirit and scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the scope defined by the claims.

Claims

1. A method for detecting a mask, characterized in that: include: Acquire a target mask, wherein the target mask is a mask after optical proximity correction; Acquire a mask image, wherein the mask image is an image of a mask plate produced based on the target mask; The consistency of the reticle image and the target mask is detected according to the image difference between the reticle image and the target mask.

2. The detection method according to claim 1, characterized in that: Also includes: The value of at least one parameter used in performing optical proximity correction is adjusted according to the image difference to obtain an updated target mask.

3. The detection method according to claim 1, characterized in that: The detecting the consistency between the reticle image and the target mask according to the image difference between the reticle image and the target mask comprises: Overlapping the mask image and the target mask to obtain an overlapped image, wherein the overlapped image is divided into a plurality of segments according to a pattern outline of the target mask; For each segment, calculating the image difference between the pattern of the reticle image and the pattern of the target mask in the segment; The consistency between the reticle image and the target mask is detected according to the image differences of the multiple segments.

4. The detection method according to claim 3, characterized in that: The acquiring of the target mask comprises: acquiring a regional image of at least one first preset area of ​​the target mask; the acquiring of the mask image comprises: acquiring a regional image of at least one second preset area of ​​the mask image, wherein the at least one first preset area corresponds to the at least one second preset area; and the overlapping of the mask image and the target mask to obtain an overlapping image comprises: For each of the first preset regions, overlapping the region image of the first preset region and the region image of the corresponding second preset region to obtain a corresponding overlapping region image; At least one of the overlapping area images is aggregated to obtain the overlapping image.

5. The detection method according to claim 3, characterized in that: The detecting the consistency between the mask image and the target mask according to the image differences of the plurality of segments comprises: Counting the numerical distribution of the image differences of the multiple segments; The consistency between the reticle image and the target mask is detected according to the statistical results.

6. The detection method according to claim 5, characterized in that: The more dispersed the numerical distribution of the image differences of the multiple segments is and the farther the peak is from the baseline, the worse the consistency between the mask image and the target mask is, wherein the baseline is used to indicate that the image difference is zero.

7. A mask detection device, characterized in that: include: A first acquisition module, used for acquiring a target mask, wherein the target mask is a mask after optical proximity correction; A second acquisition module is used to acquire a mask image, where the mask image is an image of a mask plate produced based on the target mask; The detection module is used to detect the consistency between the reticle image and the target mask according to the image difference between the reticle image and the target mask.

8. A computer-readable storage medium, wherein the computer-readable storage medium is a non-volatile storage medium or a non-transient storage medium, and a computer program is stored thereon, wherein: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are performed.

9. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instructions are executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A terminal comprising a memory and a processor, wherein the memory stores a computer program that can be run on the processor, characterized in that: When the processor runs the computer program, the steps of the method according to any one of claims 1 to 6 are performed.

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